Add python matplotlib example to plot the training log

1 files changed, 185 insertions, 0 deletions

diff --git a/scripts/plot_training_log.py.example b/scripts/plot_training_log.py.example
new file mode 100755
index 00000000..b40cbc77
--- /dev/null
+++ b/scripts/plot_training_log.py.example
@@ -0,0 +1,185 @@
+#!/usr/bin/env python
+import os
+import random
+import sys
+import matplotlib.cm as cmx
+import matplotlib.colors as colors
+import matplotlib.pyplot as plt
+import matplotlib.legend as lgd
+import matplotlib.markers as mks
+
+def get_log_parsing_script():
+    return './parselog.sh'
+
+def get_log_file_suffix():
+    return '.log'
+
+def get_chart_type_description_separator():
+    return '  vs. '
+
+def is_x_axis_field(field):
+    x_axis_fields = ['Iters', 'Seconds']
+    return field in x_axis_fields
+
+def create_field_index():
+    train_key = 'Training'
+    test_key = 'Test'
+    field_index = {train_key:{'Iters':0, 'Seconds':1, train_key + ' loss':2,
+                              train_key + ' learning rate':3},
+                   test_key:{'Iters':0, 'Seconds':1, test_key + ' accuracy':2,
+                             test_key + ' loss':3}}
+    fields = set()
+    for data_file_type in field_index.keys():
+        fields = fields.union(set(field_index[data_file_type].keys()))
+    fields = list(fields)
+    fields.sort()
+    return field_index, fields
+
+def get_supported_chart_types():
+    field_index, fields = create_field_index()
+    num_fields = len(fields)
+    supported_chart_types = []
+    for i in xrange(num_fields):
+        if not is_x_axis_field(fields[i]):
+            for j in xrange(num_fields):
+                if i != j and is_x_axis_field(fields[j]):
+                    supported_chart_types.append('%s%s%s' % (
+                        fields[i], get_chart_type_description_separator(),
+                        fields[j]))
+    return supported_chart_types
+
+def get_chart_type_description(chart_type):
+    supported_chart_types = get_supported_chart_types()
+    chart_type_description = supported_chart_types[chart_type]
+    return chart_type_description
+
+def get_data_file_type(chart_type):
+    description = get_chart_type_description(chart_type)
+    data_file_type = description.split()[0]
+    return data_file_type
+
+def get_data_file(chart_type, path_to_log):
+    return path_to_log + '.' + get_data_file_type(chart_type).lower()
+
+def get_field_descriptions(chart_type):
+    description = get_chart_type_description(chart_type).split(
+        get_chart_type_description_separator())
+    y_axis_field = description[0]
+    x_axis_field = description[1]
+    return x_axis_field, y_axis_field    
+
+def get_field_indecies(x_axis_field, y_axis_field):    
+    data_file_type = get_data_file_type(chart_type)
+    fields = create_field_index()[0][data_file_type]
+    return fields[x_axis_field], fields[y_axis_field]
+
+def load_data(data_file, field_idx0, field_idx1):
+    data = [[], []]
+    with open(data_file, 'r') as f:
+        for line in f:
+            line = line.strip()
+            if line[0] != '#':
+                fields = line.split()
+                data[0].append(float(fields[field_idx0].strip()))
+                data[1].append(float(fields[field_idx1].strip()))
+    return data
+
+def random_marker():
+    markers = mks.MarkerStyle.markers
+    num = len(markers.values())
+    idx = random.randint(0, num - 1)
+    return markers.values()[idx]
+
+def get_data_label(path_to_log):
+    label = path_to_log[path_to_log.rfind('/')+1 : path_to_log.rfind(
+        get_log_file_suffix())]
+    return label
+
+def get_legend_loc(chart_type):
+    x_axis, y_axis = get_field_descriptions(chart_type)
+    loc = 'lower right'
+    if y_axis.find('accuracy') != -1:
+        pass
+    if y_axis.find('loss') != -1 or y_axis.find('learning rate') != -1:
+        loc = 'upper right'
+    return loc
+
+def plot_chart(chart_type, path_to_png, path_to_log_list):
+    for path_to_log in path_to_log_list:
+        os.system('%s %s' % (get_log_parsing_script(), path_to_log))
+        data_file = get_data_file(chart_type, path_to_log)
+        x_axis_field, y_axis_field = get_field_descriptions(chart_type)
+        x, y = get_field_indecies(x_axis_field, y_axis_field)
+        data = load_data(data_file, x, y)
+        ## TODO: more systematic color cycle for lines
+        color = [random.random(), random.random(), random.random()]
+        label = get_data_label(path_to_log)
+        linewidth = 0.75
+        ## If there too many datapoints, do not use marker.
+##        use_marker = False
+        use_marker = True
+        if not use_marker:
+            plt.plot(data[0], data[1], label = label, color = color,
+                     linewidth = linewidth)
+        else:
+            ok = False
+            ## Some markers throw ValueError: Unrecognized marker style
+            while not ok:
+                try:
+                    marker = random_marker()
+                    plt.plot(data[0], data[1], label = label, color = color,
+                             marker = marker, linewidth = linewidth)
+                    ok = True
+                except:
+                    pass
+    legend_loc = get_legend_loc(chart_type)
+    plt.legend(loc = legend_loc, ncol = 1) # ajust ncol to fit the space
+    plt.title(get_chart_type_description(chart_type))
+    plt.xlabel(x_axis_field)
+    plt.ylabel(y_axis_field)  
+    plt.savefig(path_to_png)     
+    plt.show()
+
+def print_help():
+    print """This script mainly serves as the basis of your customizations.
+Customization is a must.
+You can copy, paste, edit them in whatever way you want.
+Be warned that the fields in the training log may change in the future.
+You had better check the data files and change the mapping from field name to
+ field index in create_field_index before designing your own plots.
+Usage:
+    ./plot_log.sh chart_type[0-%s] /where/to/save.png /path/to/first.log ...
+Notes:
+    1. Supporting multiple logs.
+    2. Log file name must end with the lower-cased "%s".
+Supported chart types:""" % (len(get_supported_chart_types()) - 1,
+                             get_log_file_suffix())
+    supported_chart_types = get_supported_chart_types()
+    num = len(supported_chart_types)
+    for i in xrange(num):
+        print '    %d: %s' % (i, supported_chart_types[i])
+    exit
+
+def is_valid_chart_type(chart_type):
+    return chart_type >= 0 and chart_type < len(get_supported_chart_types())
+  
+if __name__ == '__main__':
+    if len(sys.argv) < 4:
+        print_help()
+    else:
+        chart_type = int(sys.argv[1])
+        if not is_valid_chart_type(chart_type):
+            print_help()
+        path_to_png = sys.argv[2]
+        if not path_to_png.endswith('.png'):
+            print 'Path must ends with png' % path_to_png
+            exit            
+        path_to_logs = sys.argv[3:]
+        for path_to_log in path_to_logs:
+            if not os.path.exists(path_to_log):
+                print 'Path does not exist: %s' % path_to_log
+                exit
+            if not path_to_log.endswith(get_log_file_suffix()):
+                print_help()
+        ## plot_chart accpets multiple path_to_logs
+        plot_chart(chart_type, path_to_png, path_to_logs)